With the increasing demand for security and safety, video-based surveillance systems are being increasingly utilized in urban locations. Vast amounts of video footage can be collected and analyzed for traffic violations, accidents, crime, terrorism, vandalism, and other activities. Since manual analysis of such large volumes of data is prohibitively costly, there is a pressing need to develop an effective tool that can aid in the automatic or semi-automatic interpretation and analysis of such video data for surveillance and law enforcement among other activities.
The ability to efficiently and easily detect managed lane violations in HOV/HOT lanes has aroused considerable interest. Conventionally, managed lane regulations are enforced by a law enforcement officer via manual observations. Unfortunately, this practice is expensive and more importantly, not very effective. In one conventional approach, for example, a camera-based system is employed to determine the number of persons within a vehicle utilizing a front and a side-view camera placed on or near a toll gantry. The field of view of the side camera captures rear seat passengers while the field of view of the front view camera captures front seat passengers. Such an approach does not automatically count the number of passengers in the rear seat and is not robust against variations of window types.
Based on the foregoing, it is believed that a need exists for an improved side window detection approach with respect to NIR images utilizing machine learning, as will be discussed in greater detail herein.